Application of the full Bayesian significance test to model selection under informative sampling

Autor(es)

Sikov, A. ^[1] ; Stern, J. M. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo IME USP, Inst Math & Stat, Rua Matao 1010, Cidade Univ, BR-05508090 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: STATISTICAL PAPERS; v. 60, n. 1, p. 89-104, FEB 2018.
• Citações Web of Science: 0

Resumo

Adopting likelihood based methods of inference in the case of informative sampling often presents a number of difficulties, particularly, if the parametric form of the model that describes the sample selection mechanism is unknown, and thus requires application of some model selection approach. These difficulties generally arise either due to complexity of the model holding in the sample, or due to identifiability problems. As a remedy we propose alternative approach to model selection and estimation in the case of informative sampling. Our approach is based on weighted estimation equations, where the contribution to the estimation equation from each observation is weighted by the inverse probability of being selected. We show how weighted estimation equations can be incorporated in a Bayesian analysis, and how the full Bayesian significance test can be implemented as a model selection tool. We illustrate the efficiency of the proposed methodology by a simulation study. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 13/17746-5 - Abordagem Bayesiana para Lidar com Não-Resposta Informativa
• Beneficiário: Anna Sikov
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado